# 02-Convexity-Preserving-Operations

This is the second notebook of the series going through Convex Optimizaion. The topics here are following MOOC Convex Optimization course by Stephen Boyd at Stanford.

### Intersection:

If $S_1$, $S_2$ are convex then:

$S_1\cap S_2$ is convex.

Example:

The example below is a set constructed by intersecting many halfspaces.

In [40]:

### Afine functions:

$f: R^n \rightarrow R^m$ is affine if $f(x)=Ax+b$, where $A \in R^{m \times n}$ and $b\in R^m$, (sum of a linear function and a constant)

• Suppose $S\subseteq R^n$ is convex and $f$ an affine function, Then $f(S)$ is convex.

• If $f: R^k \rightarrow R^n$ is an affine function, then inverse image of $S$ under $f$:$f^{-1}(S) = {x\mid f(x)\in S}$ is convex.

#### Examples:

• Scaling and translations are convex preserving:

if $S\subseteq R^n$ is convex, $\alpha \in R$ an $a\in R^n$, then the sets $\alpha S$ and $S+a$ are convex:

$\alpha S=\{\alpha x\mid x\in S\}$, $S+a=\{x+a\mid x \in S\}$

• Projection of a convex set onto some of its coordinates is convex.:

if $S\subseteq R^m \times R^n$ is convex, then

$T=\{x_1 \in R^m\mid (x_1,x_2) \in S \text{ for some } x_2 \in R^n\}$

is convex.

• The sum of two sets is defined as:

If $S_1$ and $S_2$ are convex, then $S_1+S_2$ are convex.

To see why we note that:

if $S_1$ and $S_2$ are convex so is Cartesian product:

$S_1\times S_2=\{(x_1,x_2)\mid x_1\in S_1, x_2 \in S_2\}$

The image of this set under the linear funciton $f(x_1,x_2)=x_1+x_2$ is the sum $S_1+S_2$.

### Linear Fractionals and perspective functions:

#### The perspective function:

This function scales or normalizes vectors by last component and then drops normalizer.

• If $C \subseteq \textbf{dom} P$ is convex, then its image:

is convex.

Suppose $x = (\tilde{x},x_{n+1}),y = (\tilde{y},y_{n+1}) \in R^{n+1}$ and $x_{n+1}>0, y_{n+1}> 0, \text{ then for }0 \leq \theta \leq 1$ we have:

% (1-\theta)y_{n+1}}\frac{\tilde{y}}{y_{n+1}}\\ &=\mu P(x)+(1-\mu)P(y)\\ \end{align} %]]> where $\mu=\frac{\theta x_{n+1}}{\theta x_{n+1}+(1-\theta) y_{n+1}} \in [0,1]$ and monotonic. This establishes the convexity preserving of $P$. If $C$ is convex with $C \subseteq \textbf{dom} P$ from above we have that the line segment $[P(x),P(y)]$ is in $P(C)$.

• The inverse image of a convex set under the perspective function is also convex:

if $C\subseteq R^n$ is convex, then:

$P^{-1}(C)=\{(x,t)\in R^{n+1}\mid x/t\in C,t>0\}$

is convex.

#### Linear-fractional functions:

A linear-fractional function is formed by composing the perspective function with and affine function:

suppose $g:R^n\rightarrow R^{m+1}$ is affine:

Where $A \in R^{m \times n}$,$b\in R^m$,$c \in R^n$ and $d \in R$. The function $f:R^n \rightarrow R^m$ given by $f=p \circ g$:

\begin{align} f(x)=(Ax+b)/(c^Tx+d), \textbf{dom}f={x \mid c^Tx+d>0}
\end{align}

If $c=0$ and $d>0$, the domain of $f$ is $R^n$ and $f$ is an affine function.

linear-fractional functions are convex preserving.

In [131]:

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## Generalized inequalities

### Proper cones and generalized inequalities

A cone $K \subseteq R^n$ is called a propor cone of it satisfies following:

• K is convex
• K is closed.
• K is solid, it has nonempty interior.
• K is pointed, it has no line or $x\in K, -x \in K \Rightarrow x=0$

Generalized inrquality is defined over a proper cone:

\begin{align} x\preceq_K y \iff y-x\in K. \end{align} and for the strict ordering:

\begin{align} x\prec_K y \iff y-x\in \textbf{int }K. \end{align}

#### Examples

• if $K=R_+$ then partial ordering $\prec_K$ is the usual $\leq$ on R.

$x\preceq_{R_+} y \iff y_i-x_i\geq 0$.

• positive semidefinite cone $K=S_{+}^n$.

$X\preceq_{S_{+}} Y \iff Y-X \text{ is postive semidifnite}$.

#### Minimum and Minimal elements

• $x\in S$ is the minimum element of $S$ with respect to $\preceq_k$ if:

$y\in S \Rightarrow x \preceq_k y$.

• $x\in S$ is the minimal element of $S$ with respect to $\preceq_k$ if:

$y\in S, y \preceq_k x \Rightarrow x = y$.

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